Psychological and Electrophysiological Correlates of Word Learning Success

Background A rich vocabulary supports human achievements in socio-economic activities, education, and communication. It is therefore important to clarify the nature of language acquisition as a complex multidimensional process. However, both the psychological and neurophysiological mechanisms underpinning language learning, as well as the links between them, are still poorly understood. Objective This study aims to explore the psychological and neurophysiological correlates of successful word acquisition in a person’s native language. Design Thirty adults read sentences with novel nouns, following which the participants’ electroencephalograms were recorded during a word-reading task. Event- related potentials in response to novel words and alpha oscillation parameters (amplitude, variability, and long-range temporal correlation dynamics) were analyzed. Learning outcomes were assessed at the lexical and semantic levels. Psychological variables measured using Amthauer’s test (verbal abilities), BIS/BAS scales (motivation), and the MSTAT-1 (ambiguity tolerance) and alpha oscillation parameters were factored. Results Better recognition of novel words was related to two factors which had high factor loadings for all measured alpha oscillation parameters, indicating the role of attention networks and respective neural activity for enabling information processing. More successful learners had lower P200 amplitude, which also suggests higher attention-system involvement. Another factor predicted better acquisition of word meanings for less ambiguity-tolerant students, while the factor which pooled logical conceptual thinking ability and persistence in goal-reaching, positively correlated with acquisition of both word forms and meanings. Conclusion The psychological factors predominantly correlated with word-learning success in semantic tasks, while neurophysiological variables were linked to performance in the recognition task.


Introduction
During their lifespans, human beings learn on average more than 40,000 words (Kuipers, Uminski, Green, Hughes, & Aglietti, 2017). Generally, word learning may be posited as including acquisition of novel word forms (phonological and/or orthographic), new meanings (both novel semantics and connections with previous semantic knowledge), and establishing links between them . e balance between these constituent parts depends on exact circumstances: for instance, learning a new meaning for familiar polysemic words does not include word form acquisition, whereas synonym or foreign word learning may only require connecting novel word forms to already familiar concepts, which have been previously established for the native language (L1).
Successful word learning eventually provides one with a rich vocabulary, which supports one's communication abilities and is key for achieving success in various social, educational, and professional elds. Any de cits which impede language learning negatively in uence cognitive development and academic achievement. erefore, it is important to investigate and understand the key factors determining success in novel word acquisition.

External Factors
e external factors of word learning success include the learning materials and methods used. e existing literature on learning conditions has generally focused on second language (L2) learning, probably because L1 is usually acquired in a natural, implicit way, whereas L2 usually needs an explicit learning strategy (in monolingual environments). Arguably, the same factors could be important in terms of L1 learning, for instance, when studying new professional or scienti c terminology. us, the method of learning (implicit or explicit) could be one of the factors determining learning success (Dickinson et al., 2019;Sobczak & Gaskell, 2019).
Learning outcomes are also a ected by the modality of stimulus presentation (Penney, 1989). e results, however, are task-dependent and related to the appropriateness of the speci c modality to the task (Welch, DutionHurt, & Warren, 1986).
Finally, many other psycholinguistic variables, such as word length and frequency, number of lexical neighbors, concreteness, emotional validity, and imageability may a ect word learning (Ferré, Ventura, Comesaña, & Fraga, 2015). us, experimental procedures involve a variety of external factors that have a great impact on word learning success. is makes it di cult to compare the results of di erent studies and, consequently, highlights the necessity of simultaneous employment of both psychological and neurophysiological approaches in a single study.

Internal Factors
ere is ample evidence of the in uence of students' psychological features on their learning achievement; these include the level of concentration, attention allocation and maintenance, short-term and motor memory, thinking skills, cognitive control, etc. (Chen & Chen, 2015;Kostromina, Mkrtychian, Kurmakaeva, & Gnedykh, 2017;York, Gibson, & Rankin, 2015;Zohar & Dori, 2003). As for language learning in particular, it has been shown that cognitive exibility (Ehrman & Oxford, 1995), attention allocation (Schmidt, 2012), and working memory capacity (Kroll, Michael, Tokowicz, & Dufour, 2002) allow students to be more e ective in L2 learning.
As for L1 acquisition studies, they mostly concern native vocabulary learning in childhood. ese studies have revealed relationships between vocabulary acquisition and working memory (Verhagen & Leseman, 2016), attention, (Bastianello, Majo-rano, & Burro, 2018) and other executive functions (White, Alexander, & Greeneld, 2017), as well as inference making (Kim, 2017) and thinking (Kostromina & Nagaeva, 2008). However, there is a lack of evidence of the in uence of adult learners' individual di erences on word acquisition in L1.

Neuropsychological Correlates
Along with the investigation of the psychological factors involved in language learning, there is a body of neurophysiological studies exploring the brain activity underpinning this process. Many of these were conducted using electroencephalography (EEG), one of the most popular and a ordable methods for non-invasive assessment of brain activity. Due to its superb temporal resolution, an EEG is particularly well suited for studying the highly dynamic neural processes subserving the language function. Di erent electrophysiological measures which can be acquired using EEG are associated with speci c psychological functions, and include event-related potentials (ERPs) (Kappenman & Luck, 2012) and oscillatory activity (Klimesch, 2012).
Since novel word learning assumes the acquisition of both new word forms and previously unfamiliar meanings, the ERP components of orthographic and semantic processing are of particular interest. e P200 (or P2) component with a frontocentral positive-going distribution is related to orthographic form recognition (Bermúdez-Margaretto, Beltrán, Shtyrov, Dominguez, & Cuetos, 2020), and its amplitude is correlated with word frequency (Y. Wang, Jiang, Huang, & Qiu, 2021). Moreover, a positive ERP around 200 ms is connected with a top-down control over attention (Morrison & Taler, 2020), and an increase in its amplitude is associated with a decrease in the level of attention (Cnudde et al., 2021).
Another important EEG marker for word processing is N400; this is the negative ERP traditionally associated with semantic processing (Kutas & Federmeier, 2011), which amplitude reduction indicates the integration of novel words into the lexicosemantic cognitive system. Interestingly, it has been shown that a rapidly developed N400 has frontal distribution, whereas, a er a consolidation, it typically shi s into centro-parietal areas (Rasamimanana, Barbaroux, Colé, & Besson, 2020).
By measuring the oscillatory activity which is believed to re ect the functioning of large-scale neural networks, properties such as its frequency, amplitude, and the temporal dynamics of oscillatory patterns in speci c frequency bands can be assessed. Alpha-band activity (oscillations in the 8-12 Hz range) is of particular interest since these oscillations re ect the general activation of the cerebral cortex and have been associated with many cognitive processes. e most important one is attention (Bastiaansen, Böcker, & Brunia, 2002;Klimesch, 1999), which, in turn, in uences visual perception (Hanslmayr, Gross, Klimesch, & Shapiro, 2011), and consequently may a ect the success of written word acquisition. Alpha-band oscillations re ect suppression of brain structures irrelevant to a speci c task (Jensen & Mazaheri, 2010) and the selection of relevant information (Klimesch, 2012).

Objective
As reviewed above, there are multiple factors at play which potentially a ect success in word acquisition. However, there is still a lack of research investigating the psychological and neurophysiological correlates of language learning in an integrative fashion, which could help elucidate language acquisition as a complex multidimensional process.
Our study aimed to investigate both the electrophysiological and psychological correlates of contextual learning of new nouns in the native language. We hypothesized that more and less successful word learners di er in their verbal intelligence, motivation, and tolerance of ambiguity, as well as in their brain responses to novel words in an attention-demanding task.
To explore word learning success thoroughly, we chose ve tasks to assess different levels of lexical and semantic word processing. e stimulus preparation, and learning and testing procedures, were developed according to recommendations for neurophysiological studies of language learning and word acquisition (Blagovechtchenski et al., 2019). We used EEG, a non-invasive neuroimaging method with a high temporal resolution, which makes it most appropriate for studying dynamic cognitive processes (Erickson, Kappenman, & Luck, 2018). We assessed the parameters of alpha oscillations, as they provide an objective indicator of the state of attention networks (Klimesch, 2012). e main characteristics of these oscillations (amplitude, variability, and long-range temporal correlations) were analyzed and linked to novel word learning performance.
Since the level of alpha-range activity re ects the level of attention and visual alertness (Bastiaansen et al., 2002;Stothart & Kazanina, 2013;J. Wang, Conder, Blitzer, & Shinkareva, 2010), we anticipated that the amplitude of alpha oscillation would be negatively correlated with word learning success.
It has been shown that uctuations of ongoing brain oscillations are linked to variability in behavioral responses, particularly, in visual stimulus detection (Zazio, Schreiber, Miniussi, & Bortoletto, 2020). A decrease of alpha power implies a more liberal detection criterion, whereas the opposite is true for its increase (Iemi & Busch, 2018;Iemi, Chaumon, Crouzet, & Busch, 2017). us, high variability of alpha oscillation may be linked to more variable responses to the same stimuli. is, in turn, suggests a negative correlation of such variability and behavioral accuracy in timelimited attention-demanding tasks such as Recognition and Lexical decision.
For ERPs to newly learnt words, we expect to see di erences in P200 and N400 amplitudes between more and less successful word learners since, for the latter, new nouns may remain orthographically (P200) and semantically (N400) less familiar.

Participants
irty right-handed healthy volunteers (M age = 23.4 year; range = 18-35 years; 53.33% females), all monolingual Russian speakers, participated in the study. All subjects gave their written informed consent and lled out a questionnaire about their demographic characteristics and health. e study protocol was approved by the Ethical Committee of Saint Petersburg University.

Stimuli
Novel words were simultaneously provided with both new word forms and novel meanings. To create novel word forms, four groups of 10 Russian nouns with the same structure (CVCCVCVC, where C is a consonant and V is a vowel) were chosen. e groups did not di er statistically in their lemma and last-syllable frequency. Novel word forms were created by mixing ultimate syllables within the group: for exam- us, 40 novel word forms were produced. ey were rotated across subjects in terms of their experimental role and were used as either novel words (concrete or abstract) or untrained llers; moreover, novel word forms were assigned to the meanings in a counterbalanced fashion. Rare or obsolete objects (concrete semantics, 10 items) or abstract concepts borrowed from foreign cultures (10 items) were used for the novel meanings.

Learning Procedure
Each novel word was presented visually in ve eight-word sentences (Figure 1), which gradually revealed its meaning from the described situational context. Every sentence was rst presented word-by-word and then, to ensure understanding, com- pletely on the computer screen. Participants had to read these sentences sitting in an acoustically and electrically shielded chamber and press a button a er reading the whole sentence. Presentation of sentences was managed using NBS Presentation 20.0 so ware with a black Arial font (size 27) on grey background.

EEG Recording
e 128-channel active EEG actiCHamp setup and BrainVision Recorder, so ware (BrainProducts, GmbH, Gilching, Germany) were used to investigate the neurophysiological correlates of word learning. e electrodes were applied according to the extended 10-10 system (M1-ext montage by Easycap GmbH, Germany) with FCz as a reference channel, and one EOG electrode was placed under the le eye. 1 kHz sampling rate was used.
e EEG was recorded during a silent reading task. e participants' attention to the reading task was ensured by use of rare target stimuli (city names; 40 items, repeated twice) randomly dispersed among the main experimental stimuli; novel words (20 items, repeated 10 times); and untrained llers (60 items, equally composed of real words and orthographically similar pseudowords, repeated 10 times). ey were randomly presented (black Arial font (size 24), grey background, 600 ms per word, 1400-ms interstimulus interval with a xation cross) with the instruction to read all stimuli carefully and press the button (response pad RB-740, Cedrus Corp., San Pedro, CA) with the le index nger each time a city name appeared on the screen.

EEG Analysis
Preprocessing. Custom-built scripts in MATLAB 6.0 (MathWorks Inc., Natick, MA) and the Berlin Brain-Computer Interface (BBCI) toolbox (https://github.com/bbci, GitHub) were used for EEG analysis. First, a band-pass lter between 1 and 45 Hz (2th-order Butterworth lters), down-sampling to 250 Hz sampling rate, and rereferencing to the common average reference, was applied to the raw EEG data. en, the EEG data were visually inspected to remove artefacts, particularly those associated with muscle activity. Independent component analysis was performed and components associated with blinking and eye movement were removed. Alpha oscillation analysis. We estimated three parameters of alpha oscillations: amplitude, LRTC, and variability (coe cient of quartile variation, CQV).
Amplitude: e amplitude (extracted with 8-12 Hz band-pass Butterworth second-order lter) was computed using an analytic signal approach based on the Hilbert transform for each subject and each channel over the entire continuous recording. For each subject, all data from all channels were averaged, so we eventually had one mean value of alpha amplitude for each subject (Figure 2A).
LRTC: To estimate LRTC, we used detrended uctuation analysis (DFA) of the amplitude envelope of alpha neuronal oscillations (Kantelhardt, Koscielny-Bunde, Rego, Havlin, & Bunde, 2001;Peng, Havlin, Stanley, & Goldberger, 1995). Note that LRTC refers to the correlation between di erent time points in EEG activity, not across di erent spatial locations. Technical details on the use of DFA for the estimation of LRTC in EEG signals can be found in Hardstone et al. (2012). Finally, we had one mean DFA exponent for each subject ( Figure 2B).
CQV: e variability was estimated from the amplitude envelope of alpha oscillations extracted as described above. It was quanti ed with the coe cient of quartile variation (CQV), a descriptive statistic based on quartiles' information (Bonett, 2006): In (1), Q1 and Q3 denote the rst (lower) and third (upper) quartiles of the data, respectively. Quartiles are the points that divide any ranked data set into four equal groups. Finally, we had one mean CQV coe cient for each subject.
ERP analysis. First, we segmented the preprocessed EEG recording into epochs from -200 ms before the stimulus event (with -200-0 ms interval used as the baseline) to 1000 ms a er that. Second, standard deviation analysis was implemented for each segment using the Berlin BCI toolbox in MATLAB (https://github.com/bbci, GitHub). Since both of the expected ERP components (P200 and N400) could be detected above the fronto-central region, and the frontal and prefrontal cortex play a crucial role in the assimilation of new word forms (Eichenbaum, 2017;Plakke, Romanski, & Kikuchi, 2014), we analyzed ERPs measured above this region to assess their activity in the process of novel word learning. We used averaged ERPs from F1, F2, and Fz electrodes (ROI above frontal/prefrontal cortex) to quantify di erences in ERP amplitudes between the groups (see below).

Learning Outcome Assessment
Five tasks were chosen to assess the success of learning: 1) Free Recall (performed before the EEG task to avoid the impact of the passive reading task in EEG on the novel word recall accuracy); 2) Recognition; 3) Lexical Decision; 4) Semantic De nition; and 5) Semantic Matching (Table 1). Microso Excel Spreadsheets were used for tasks 1 and 4, and NBS Presentation so ware for the others (with the same screen and text parameters as in the learning procedure above). e stimulus set and the presentation procedure for the Recognition and Lexical Decision tasks were the same as for the EEG reading task, with the exception that the latter also included target stimuli (city names). e integrative variable General Success was calculated as the mean of z-scores of all task results (accuracy and quality, see Table 1).

Psychological Assessment
To evaluate conceptual thinking abilities, we used the second (Excluding the Word) and third (Analogies) subtests of the Russian version of Amthauer's IST test (Golovei & Rybalko, 2006). Tolerance of ambiguity was assessed using the MSTAT-I questionnaire (McLain, 1993). Osin (2010) had previously adapted this questionnaire for Russian students and determined the questionnaire's complex two-dimensional structure. For this study, the dimension of attitude toward ambiguous situations was chosen. us, we measured two variables -Preference for ambiguous situations (direct scales), and Acceptance/avoidance of ambiguous situations (inverted scales).
BIS/BAS scales were developed by Carver and White (1994) to measure two motivational systems: a behavioral inhibition system (BIS) corresponding to avoiding aversive outcomes, and a behavioral activation system (BAS) which, in turn, consists of three subscales: Fun-Seeking, Drive, and Reward Responsiveness. Fun-Seeking is associated with impulsivity, whereas other subscales are related to reward sensitivity and reaching goals. In this study, we used the Russian version of the BIS/BAS questionnaire adapted by Knyazev and colleagues (2004). e psycho-diagnostic techniques were selected based on the existing literature. e two scales of Amthauer's test measure semantic conceptual abilities, which play a crucial role in academic success (Kholodnaya, Trifonova, Volkova, & Sipovskaya, 2019). BIS/BAS scales, in turn, measure two motivational systems that underlie human behavior and a ect (Carver & White, 1994); this is important due to the role of motivation in language learning, as highlighted in the Introduction. MSTAT-1 was selected because of its present use of contextual learning, which requires readiness to act in ambiguous, unclear situations, such as an encounter with previously unknown words within short story-like sets of sentences. e latter resembles a situation when a child is faced with a new word, which is unfamiliar to them at both the word-form and meaning levels.

Statistical Analysis
Statistical analysis was conducted using IBM SPSS Statistics 26.0 so ware. e reliability of BIS/BAS and MSTAT-1 was evaluated using Cronbach's alpha coe cient. High reliability was revealed for Preference of Ambiguous Situations (0.750), Acceptance/Avoidance of Ambiguous Situations (0.869), Drive (0.775), Reward Responsiveness (0.764), and BIS (0.767). However, Cronbach's alpha was low (0.241) for Fun-Seeking; therefore this subscale was excluded from further analysis.
To minimize the number of variables, psychological and alpha oscillation (amplitude, CQV, and LRTC) parameters were factored using the principal components method with varimax rotation (Kaiser, 1958). en, correlations between the obtained components and the behavioral task results were calculated using the nonparametric Spearman Rho test.
For paired comparison between groups of subjects with di erent success levels, the sample was divided into two main groups: less successful learners (LSL), who scored General Success values less than M -0.25σ (17 people, 58.82% male), and more successful learners (MSL), who had General Success scores above M + 0.25σ (10 people, 30% male); three participants with intermediate values were excluded from further analysis. e LSL and MSL groups were compared with Pearson χ2 and Fisher's exact tests for the nominative variable (Gender) and U Mann-Whitney for the others.
Between-group (MSL vs. LSL) comparison of ERP amplitudes, computed in 8-ms bins between 0 and 800 ms, was done using the Wilcoxon test for independent samples (two-tailed) implemented in the MATLAB environment. FDR correction for multiple comparison was implemented.

Socio-demographic Characteristics
Comparison between the LSL and MSL groups showed that they did not di er statistically in Gender (χ2 = 2.095, p (Fisher's exact test) = 0.236), Age, and Handedness, but more successful word learners had more years of Education ( Table 2).   Table 4 Correlations between factors and learning outcomes Note. * p < .05. ** p < .01 Factor Analysis e Kaiser-Meyer-Olkin coe cient was 0.510, which indicated that the factor analysis was appropriate for these data. e Bartlett's test of sphericity showed that the variables had correlations with each other (p = 0.004, chi-square = 74.179, df = 45); thus, they were suitable for structure detection. e principal components method with varimax rotation extracted ve factors with a cumulative contribution rate of 80.436% (Table 3).

Correlation between Factors and Success
e interrelationships between word learning success (both as an integrative variable and for each task separately) and the ve factors were analyzed using a non-parametric Spearman's rank correlation test ( Table 4: p-values reported without correction for multiple comparisons; the correlations which survived FDR corrections for multiple comparisons are underlined).
e Reward/Punishment Sensitivity factor did not interact signi cantly with any of the task results. e factors Physiological Indicators of Low Attention Concentration and Cognitive Processing Neurodynamics signi cantly correlated with Recognition scores. Tolerance of Ambiguity negatively correlated with General Success and the accuracy of Semantic De nition. e Persistence in Conceptual inking factor had positive interrelationships with accuracy scores on three tasks (Free Recall, Semantic De nition, and Semantic Matching) and the composite General Success measure.
us, we found а relationship between psychological and neurophysiological characteristics at the basic level of word acquisition related to surface word-form memory (as measured by the recognition task). Moreover, signi cant correlations were demonstrated for factors with high factor loadings of alpha oscillation parameters. e connection between psychological variables and word-learning success, in turn, concerned the acquisition of novel semantics. e Persistence in Conceptual inking factor was revealed as the most in uential variable in word-learning success. Moreover, correlations between this factor and behavioral task results (Semantic De nition accuracy and General Success) stayed fully or marginally signi cant even a er FDR corrections (adjusted p = 0.035 and 0.068, respectively). is factor depicts persistence as a personal trait and sign of conceptual thinking capacity.

Between-group Comparison of ERPs to Novel Words
To assess overall di erences in brain activity between the LSL and MSL groups, amplitudes of ERPs over the frontal sensor ROI were compared across 800 ms a er stimulus onset, using the two-tailed independent-sample Wilcoxon test step in bins of 8 ms. Signi cant di erences were found at 153-161 ms (Z = -1.98, p = 0.047 uncorrected), 161-169 ms (Z = -2.54, p = 0.011 uncorrected), and 169-177 ms (Z = -2.08, p = 0.037 uncorrected) from the stimulus onset: more successful learners had lower frontal ERP amplitude (Figure 3). None of these results, however, survived a er FDR corrections.

Discussion
Our study aimed to investigate success in novel word acquisition in connection with learners' attention level (measured as amplitude of P200 component and alpha oscillation parameters), verbal cognitive abilities, motivation, and tolerance of ambiguity. e results showed that both psychological and physiological variables interacted with word learning success. Socio-demographic characteristics, on the other hand, did not correlate with this parameter. However, groups of more and less successful learners di ered signi cantly in the number of years of education; more successful participants had more studying experience. is may suggest that the experience of learning novel material during formal education (and possibly of being tested on it) could positively in uence the ability to learn novel words. is may be due to an increase in the number of vocabulary learning strategies that accelerate vocabulary growth (Nie, 2017), and more general cognitive learning strategies which help in acquiring information e ciently (Kostromina & Dvornikova, 2016). e two groups di ered in frontal ERP only in the time window around 150-180 ms. is wave likely corresponds to the P200 component, whose amplitude is known to negatively correlate with the level of attention (Crowley & Colrain, 2004) and to re ect inhibition of irrelevant information (Meghdadi et al., 2021;Zhao, Zhou, & Fu, 2013). us, more successful word learners were more attentive than others and were better at suppressing irrelevant inputs. Interestingly, this result was found during the reading task a er the training session; we may hypothesize that the same trait was also expressed during the learning per se.
As for P200 as a marker of orthographic encoding, in a recent study it was shown that novel ( rstly seen) words elicited lower P200 than previously known ones; however, this e ect had disappeared a er a short phonological training session (Bermú-dez-Margaretto et al., 2020). e fact that the EEGs were recorded a er the training session that provided an equal number of encounters with novel words for both groups of participants, suggests that the presence di erences in P200 amplitude more likely re ected the level of attention than the depth of orthographic processing during visual recognition.
We also expected to nd di erences in the N400 amplitude, since this component is known to re ect lexico-semantic properties of verbal (and other meaningful) stimuli and their integration into a person's lexicon. However, no N400 e ects were found, and no marked de ection was recorded in the N400 range, as can be seen in Figure 3. e absence of signi cant di erences in the amplitude of the N400 component probably stems from the absence of context in the passive word reading task applied in the EEGs (Abel, Schneider, & Maguire, 2018;Bermúdez-Margaretto, Beltrán, Cuetos, & Domínguez, 2018).
Factors with high factor loadings of alpha oscillations parameters had signi cant correlations with accuracy in the Recognition task only. us, better recognition of novel words appeared to relate to higher attention concentration as re ected in the amplitude of alpha oscillations (Bastiaansen et al., 2002;Klimesch, 1999), and to excitation/inhibition balance as re ected in LRTC (Beliaeva et al., 2019). is might indicate that the perception of novel words required high involvement of the executive control systems, upregulating the level of attention.
We also found that the General Success and Accuracy of the Semantic De nition task negatively correlated with the ambiguity tolerance measure. e least ambiguity-tolerant students had better performance in the acquisition of word meanings.
ese results contradict previous studies that indicated a positive in uence of ambiguity tolerance on L2 acquisition. However, there is evidence that the correlation of ambiguity tolerance with academic success could vary (Osin, 2010). Dealing with a large number (here, 20) of unknown words could elicit anxiety in learners with low levels of ambiguity tolerance. at, in turn, could have motivated them to resolve the ambiguities in order to understand the meanings of new words better, fostering better learning. Interestingly, the Quality variable (Semantic De nition task) showed no correlations with psychophysiological factors. It seems that the wording of denitions is a complex cognitive process that connects with other psychological and neurophysiological parameters that were not included in the study; alternatively, the measure applied here to estimate de nitions' quality may not have been su ciently precise or sensitive to demonstrate such connections.
e Persistence in Conceptual inking factor positively correlated with the results of three tasks and the integrative variable General Success. is result implies that logical conceptual thinking ability and persistence in reaching goals support the acquisition of both the forms and meanings of novel words. Moreover, only this factor stayed signi cantly correlated with learning outcomes a er FDR corrections.
us, our study showed that psychological features had stronger interrelationships with word acquisition success than neurophysiological ones. It appears necessary to continue interdisciplinary investigation of the word acquisition process to better elucidate connections between its psychological, behavioral, and neurophysiological aspects, which remain poorly understood .

Conclusion
is study investigated psychological and neurophysiological factors involved in successful word acquisition. e results have shown a range of psychological features related to performance in semantic tasks on novel word comprehension, whereas neurophysiological variables seem to be linked to successful recognition of newly acquired word forms. e more successful group of learners also showed lower P200 amplitude than their less successful peers, suggesting di erences in the level of attention, which may have contributed to better learning.

Limitations
Whereas the present study has produced novel results on psychological and neurophysiological factors related to successful word acquisition, it still has several confounds and limitations that necessitate caution in interpreting its results. e relatively small sample size restricted the number of variables that could be analyzed. us, only 10 parameters were used in the research. Crucially, only some of the alpha-band parameters were explored, while neither brain oscillations in other bands -such as beta and theta, which are closely related to memory and learning (Herweg, Solomon, & Kahana, 2020), including verbally ) -nor ERPs above other brain areas, were examined.
Moreover, whereas relationships between word learning success and psychophysiological factors could vary depending on age (Kamal, Morrison, Campbell, & Taler, 2021;Morrison & Taler, 2020), the results of the study are restricted to the young sample used in the experiments (18-35 years old). Finally, all participants were monolingual Russian speakers, and the novel words were orthographically and phonologically native-like, and were presented in L1 sentence context. us, the results of the study may not be generalizable to other languages or L2 learning without further investigation.

Ethics Statement
e study obtained ethics approval from the Ethics Committee of St. Petersburg State University (protocols № 90 from 20.02.2019 and № 82 from 25.04.2018). Written informed consent was obtained from all subjects involved in the study.

Author Contributions
Y.S. and S.K. conceived of the idea. E.B, N.M., D.T., and D.G. performed the computations. E.B., N.M., and D.T. performed the visualization. All authors discussed the results and contributed to the nal manuscript.

Con ict of Interest
e authors declare no con ict of interest.